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机器学习的最新进展用于骨髓细胞形态分析.

Yifei Lin1,2, Qingquan Chen1,3, Tebin Chen1

  • 1The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China.

Frontiers in medicine
|July 1, 2024
PubMed
概括

机器学习,特别是深度学习,为分析骨髓细胞形态提供了强大的工具,有助于早期发现疾病. 本综述指导血液学家选择人工智能算法进行自动化分析,提高诊断准确性和效率.

关键词:
人工智能的人工智能是人工智能.自动分类的自动分类.自动识别自动识别.骨髓细胞形态 骨髓细胞形态深度学习是一种深度学习.机器学习是机器学习.视觉化的可视化

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科学领域:

  • 医疗成像医学成像
  • 计算生物学 计算生物学
  • 血液学 血液学 血液学

背景情况:

  • 机器学习 (ML) 和人工智能 (AI) 在医学中越来越多地用于分析大型数据集.
  • 深度学习在图像处理方面表现出色,为医疗应用提供了强大的功能学习.
  • 手动骨髓细胞形态分析虽然是标准的,但有局限性,需要自动化解决方案.

研究的目的:

  • 审查当前关于机器学习应用在骨髓细胞形态分析中的研究.
  • 为血液学家提供关于选择合适的ML算法进行自动化的建议.
  • 确定人工智能驱动骨髓分析的未来研究方向.

主要方法:

  • 该综述综合了当前关于六个关键自动化骨髓细胞形态过程的研究.
  • 它强调了机器学习系统应用到骨髓细胞形态学的进步.
  • 该研究的重点是深度神经网络和其他ML模式用于图像分析.

主要成果:

  • 人工智能和ML显示出在骨髓分析中增强临床诊断的巨大潜力.
  • 自动化方法在细胞检测,细分,识别,分类,计数和诊断方面提供了改进.
  • 机器学习系统对细胞病变趋势的快速和精确分析充满希望.

结论:

  • 机器学习,特别是深度学习,是自动化骨髓细胞形态检查的宝贵工具.
  • 选择正确的ML算法对于有效和准确的血液学疾病诊断至关重要.
  • 需要进一步的研究才能充分实现AI在骨髓细胞病理学方面的潜力.